Computer vision assisted item search

ABSTRACT

System and techniques for computer vision assisted item search are described herein. A composite image, including visual data and depth data, may be obtained. The composite image may be filtered to isolate a clothing article represented in the composite image. A classifier may be applied to the depth data to produce a set of clothing attributes for the clothing article. The clothing attributes may then be provided to a remote device.

PRIORITY APPLICATION

This patent arises from a continuation of U.S. application Ser. No.16/853,018, filed Apr. 20, 2020, which is a continuation U.S.application Ser. No. 15/948,663, filed Apr. 9, 2018, which is acontinuation of U.S. application Ser. No. 14/970,023 (now U.S. Pat. No.9,940,728), filed Dec. 15, 2015, all of which are incorporated herein byreference in their entireties. Priority to U.S. application Ser. No.16/853,018, U.S. application Ser. No. 15/948,663, and U.S. applicationSer. No. 14/970,023 is claimed

TECHNICAL FIELD

Embodiments described herein generally relate to computer vision andmore specifically to computer vision assisted item search.

BACKGROUND

Cameras generally capture light from a scene to produce an image of thescene. Some cameras can also capture depth or disparity information.These multi-mode cameras are becoming more ubiquitous in theenvironment, from mobile phones, to gaming systems, etc. Generally, theimage data is provided separately from the depth data to consumingapplications (e.g., devices, software, etc.).

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. Like numerals havingdifferent letter suffixes may represent different instances of similarcomponents. The drawings illustrate generally, by way of example, butnot by way of limitation, various embodiments discussed in the presentdocument.

FIG. 1 is a block diagram of an example of an environment including asystem for computer vision assisted item search, according to anembodiment.

FIG. 2 illustrates an example of a method for computer vision assisteditem search, according to an embodiment.

FIG. 3 illustrates an example of a method for computer vision assisteditem search, according to an embodiment.

FIG. 4 is a block diagram illustrating an example of a machine uponwhich one or more embodiments may be implemented.

DETAILED DESCRIPTION

While devices exist to capture both two dimensional (e.g., visual)images and three dimensional (e.g., depth) images, use of these datastreams is generally relegated to specific applications, such as gesturerecognition or modeling. Combining visual and depth data to provideadditional context to observed objects may yield a richer userexperience in a number of area. One such area includes items that aresized and styled for people, such as clothing, bicycles, etc. In thisarea, non-invasive sensors (e.g., a camera and depth sensor) may capturenot only the style (e.g., pattern) of a clothing article, but also usedepth data to establish its shape and size. This sensor data may becombined with a user profile to establish actual clothing articles toacquire for the user via a search powered by the captured visual anddepth data. Thus, for example, a user who prefers wearing a jacket thatis discontinued may be relieved of the burden of searching throughinnumerable jackets to find a replacement that will fit her.

Described herein is a method and system to automatically use both visualdata and embedded depth data to establish contextual information aboutwhat people are wearing or using in a picture or video (e.g., media).This contextual information may, in an example, be used by retailers toprovide targeted sales and deliveries to specific individuals present inthe media.

Examples of depth-enabled contextual information include textures,shapes, and sizes. This information may be combined with visual-basedcontextual information, such as colors, material patterns, styles ofclothing articles, linked outfits, recreational items (bikes, runningshoes, balls, etc.), other individuals in the picture, locations presentin the picture, etc., to identify similar items that conform to the fitand style of the subject person.

The contextual information may be created in whole or part in a localdevice of the user (e.g., mobile phone, tablet, etc.) or a networkservice (e.g., cloud service). In an example, the creation andmaintenance of a user profile or the presentation of search resultsbased on the contextual information or the profile, or the delivery ofclothing articles to the user will include a user interface to enablethe user to control how and where these processes will interact with theuser (e.g., opt-in, opt-out, etc.). A user's identity may be determinedvia a number different mechanisms. For example, the device from whichthe media was transferred (e.g., uploaded) may be registered with theservice and provide identification for the user. In an example, facialrecognition or similar techniques may be used to identify the user. Inan example, the location that the media was captured may accompany or beembedded in the media and provide identification (e.g., the location maybe an apartment of the user). These identification techniques may becombined in any number of ways—such as, in a business with threeemployees, one of which is male; the location and a visual processingtechnique identifying the user as male may be combined to identify themale employee.

As noted above, a user profile may be maintained of the user. The depthand visual contextual information extracted from the media and may beinput into an analysis engine to identify patterns and preferences forthe user. The analysis engine may check for other media (e.g., in thecloud) of the user. Patterns and preferences of the user may be compiledfrom found media may be used to update the user's profile. As multiplepictures (by potentially different sources) containing the sameindividual are used, the accuracy and richness of the patterns andpreferences are increased.

A search engine may user the user's profile data, including theintegrated depth and visual data, to identify other clothing articles.In an example, the results of the search may be used to communicate theexistence of these items to the user. In an example, the search mayinclude related items that are different. For example, analytics appliedto the user profile may be used to interpolate possible new clothingpreferences (e.g., styles) based on, for example, new trends in fashion,sports, etc. In an example, the communication may include an opportunityfor the user to acquire the item. In an example, the communication mayinclude delivery of the item to the user in accordance with the userprofile.

FIG. 1 is a block diagram of an example of an environment including asystem for computer vision assisted item search, according to anembodiment. The system 100 may include a local device 105. The localdevice 105 may include, or be communicatively coupled to, a detector 115(e.g., a camera, or other mechanism to acquire visual data (measuringluminance of reflected light) in a scene) and a depth sensor 120. Asused herein, visual data, alone, is a luminance-based representation ofthe scene that may also include wavelength (e.g., color) information.

The depth sensor 120 is arranged to sample reflected energy from theenvironment. The sampling may be contemporaneous (e.g., as close to thesame time as possible) with the light sample of the detector 115. Thedepth sensor 120 is arranged to use the sampled reflected energy tocreate depth data of the scene. As used herein, pixels, or theirequivalent, in the depth data represent distance from the depth sensor120 and an element in the scene, as opposed to luminance as in an image.In an example, the depth data pixels may be called voxels as theyrepresent a point in three-dimensional space.

The depth sensor 120 may make use of, or include, an emitter 125 tointroduce a known energy (e.g., pattern, tone, timing, etc.) into thescene, which is reflected from elements of the scene, and used by thedepth sensor 120 to established distances between the elements and thedepth sensor 120. In an example, the emitter 125 emits sound. Thereflected energy of the sound interacting with scene elements and knowntiming of the sound bursts of the emitter 125 are used by the depthsensor 120 to establish distances to the elements of the scene.

In an example, the emitter 125 emits light energy into the scene. In anexample, the light is patterned. For example, the pattern may include anumber of short lines at various angles to each other, where the linelengths and angles are known. If the pattern interacts with a closeelement of the scene, the dispersive nature of the emitter 125 is notexaggerated and the line will appear closer to its length when emitted.However, when reflecting off of a distance element (e.g., a back wall),the same line will be observed by the depth sensor 120 as much longer. Avariety of patterns may be used and processed by the depth sensor 120 toestablish the depth information. In an example, time of flight may beused by the depth sensor 120 to establish distances using a light-basedemitter 125.

The local device 105 may include an interface 110. The interface 110 isarranged to interact with the detector 115 and the depth sensor 120 toobtain both image and depth data, for example, as a composite image. Theinterface 110 may buffer the data, or it may coordinate the activitiesof the detector 115 and the depth sensor 120. The interface 110 may beimplemented as a chipset, a driver, or a combination of both.

The local device 105 may include a filter 140. The filter 140 isarranged to isolate a clothing article represented in the compositeimage. Such isolation may be called segmentation. A variety of computervision techniques may be used by the filter, including application ofGabor filters, noise boundary mechanisms, edge detection, neural networkclassification, etc. In an example, the filter may be located in theremote device 155. In an example, the functionality of the filter may besplit between the local device 105 and the remote device 155.

In an example, the filter 140 may perform an initial classification onthe isolated clothing article to, for example, determine a type for theclothing article. Example types may include, pants, underwear, shirt,shoes, jacket, dress, shorts, headwear, bag, wallet, watch, necklace,eye glasses, scarf, etc. This initial classification may be referred toas a rough classification. The rough classification result may beincluded to the composite image, for example, as metadata, or passedalong with the composite image to the classifier 145 with the isolatedclothing article portion of the composite image (e.g., the pixels andvoxels of the visual and depth data that represent the clothing articlein the composite image).

The local device 105 may include a classifier 145. The classifier 145 isarranged to accept the depth data and produce a set of clothingattributes that correspond to the clothing article. The illustratedexample clothing article is a shirt 135 worn by the user 130. Theclassifier 145 may be implemented with a neural network (e.g., deepBoltzmann machine or the like), a stochastic classifier, an expertsystem, or other classification mechanism used in semantic processing ofvisual data by a computer. In an example, the classifier 145 may beimplemented in the remote device 155 as opposed to the local device 105.In an example, the classifier 145 may be implemented partially on thelocal device 105 and partially on the remote device 155.

The clothing attributes produced by classifier 145 may be calledcontextual information with respect to the clothing article. In anexample, the clothing attributes may include one or more of a color orpattern. In an example, these attributes may be a sub-category ofclothing attributes known as visual-based contextual information.

In an example, the clothing attributes may include one or more of asize, a shape, or a texture. In an example, these attributes may be asub-category of clothing attributes known as depth-based contextualinformation. The depth-based contextual information may be determinedsolely, or in majority part, from the depth data. Size, for example, maybe determined by geometrically measuring the distances between edgeddefined by voxels of a shirt. In an example, a two-dimensional pixelrepresentation of the shirt edges may be scaled using the depthinformation to establish an absolute size. However, in an example, aclassifier, such as a neural network classifier, may be trained toaccept the voxels of the isolated clothing article and produce aclothing size. As used herein absolute size refers to a measurement ininches, centimeters, or the like of the article, and clothing sizerefers to metrics used in the garment industry, such as large, medium,small, extra-long, etc. In an example, the classifier 145 is arranged toconvert an absolute size into a clothing size and provide it as one ofthe clothing attributes.

As noted above, clothing article texture, such as smooth, shiny, rough,fuzzy, furry, etc. may be provided by the classifier 145 as clothingattributes. In an example, where patterned light is used by the depthsensor 120, such texture may be determined by noting a diffusion of thepattern. Thus, while a close article may reflect a small bar, thediffuse edges of the bar or total luminance of the bar may conform to aknown pattern of reflecting for a knit at the detected distance, asopposed to leather, which may have sharper edges or greater reflectivityat the same distance. Also, with the identification of the clothingtype, such diffusion or reflectivity patterns may be used to identifylikely materials. Accordingly, a suit jacket may be classified as woolmore often than a shirt would. Thus, in an example, the texture may beused to provide a fabric of the clothing article as a clothingattribute.

As noted above, clothing article shape may also be provided as aclothing attribute by the classifier 145. In this example, the shape isthe three dimensional shape of the clothing article, similarly to adepth sensor provided model used for three dimensional printing. Thevoxels used in the shape classification may also be used to select aknown shape based on their relation to each other. For example, awestern style vest may be distinguished from a flat bottomed vest basedon voxels indicating that the vest surface extends downwards on eitherside of the central fastening seam (e.g., where the buttons are placed).

The local device 105 may include a transceiver. The transceiver isarranged to communicate with remote machine (e.g., remote device 155)via a network 150. The transceiver is arranged to provide the clothingattributes to the remote device 155. The transceiver may also bearranged to accept the delivery messages (e.g., described below) andprovide the user response to the delivery messages for the local device105.

The remote device 155 may accept the clothing attributes from the localdevice 105 (or from itself when one or more of the filter 140 orclassifier 145 are located in the remote device 155) or other deviceproviding the clothing attributes. The remote device 155 includes a userprofile 160 data structure and the interfaces used to create, update, orother manipulate the user profile 160. Thus, the remote device 155 isarranged to update the user profile 160 with the clothing attributesreceived form the local device 105. In an example, the user profileincludes a separate data structure for each clothing item identified ascorresponding to the user 130 and the clothing attributes are placed inthis data structure. In an example, the user profile includes a contextdata structure. The context data structure may include one or morefields corresponding to geographic location, landmark, event, orcompanion to the user 130 and a clothing article. Thus, for example,when processed by the clothing search engine 165, a dress of aparticular style worn to a company Christmas event last year of acertain size, color, and material may be used as inputs to search for anew dress for the upcoming Christmas event this year.

As noted above, the remote device 155 includes a clothing search engine165. The clothing search engine 165 is arranged to search for additionalclothing items using the clothing attributes and the user profile 160.Such a search may include incorporating a user's calendar (as describedabove), using the clothing attributes as inputs (e.g., size 42 wooltweed sport coat as keywords), or as parameters to an artificialintelligence engine. In this manner, the system 100 may find clothingthat the user 130 otherwise may not have. In an example, the searchincludes a restriction that the additional clothing items differ fromthe clothing article. Such a restriction may be implemented, forexample, by negating a keyword, or other search input, derived from theclothing attributes. In an example, the restriction may be implementedby replacing a keyword with another word of the same type (e.g.,replacing “red” with “blue” as a color when a clothing article's coloris red). In an example, the restriction is directed to one or more ofcolor, pattern, style, or type clothing attributes. In an example, wherethe additional clothing differs in type from the article of clothing,the search results may be filtered such that passing search results willcombine with the clothing article to add to a class of outfits. Forexample, is the clothing article is a pair of slacks, the additionalclothing articles are filtered to create outfits with the slacks whencombined. Such a filter may be implemented as a model in whichselections in part of the model are constraints on other parts of themodel.

In an example, the search results of the clothing search engine 165 maybe provided to a third party. In an example, the results, or a portionthereof, may be provided to the user 130, in the form of a deliverymessage. In an example, the delivery message includes a mechanism (e.g.,application, instructions, etc.) enabling the user 130 to confirm thatthe clothing items of the delivery message are desired by the user 130.In an example, the search results, or a portion thereof, may beautomatically delivered to the user 130, the remote device 155initiating such a deliver (e.g., via an application programminginterface (API) of a retailer) in an example.

FIG. 2 illustrates an example of a method 200 for computer visionassisted item search, according to an embodiment. The operations of themethod 200 are performed on computing hardware, such as that describedabove with respect to FIG. 1 or below with respect to FIG. 4 (e.g.,circuit sets). The method 200 follows a use case; however, the specificcharacters or items presented are for illustrative purposes as othercombinations of characters or items may be used in practice.

Jill is an enthusiast photographer who likes to take video of herfriends and family. She is using her tablet with a visual and depth datacapable sensor array to take video of her friends and family (e.g.,operation 205). Her brother-in-law, George, and their friend Shellie arein a number of the videos that Jill has taken over the past two months.Jill, Shellie, and George are enrolled in an automated item locationservice to which a local-component application on her tabletautomatically uploads her photos and other media (e.g., operation 207).The service uses the embedded depth-metadata to extract contextualinformation about items in Jill's pictures. The service may determinewhether any person in the video has opted into the program of automaticitem location, such as Jill, George, or Shelley who has signed up forthe program (e.g., operation 210). For each frame (e.g., compositeimage) of the video, the service may extract context (e.g., contextualinformation such as size, texture, shape, color, pattern, etc.) aboutitems corresponding to people identified in the frame (e.g., operation215). The service also searches for and finds similar media thatincludes George in the cloud service database.

Pattern analysis of the contextual data is automatically performed todetermine the patterns and preferences of the individual in the frames(e.g., operation 220). The pattern analysis detects that George prefersa certain style of sports jacket, of Navy and Olive colors in a 42Regular size, and color matching size 10 leather Oxford shoes.Contextual information or other conclusions of the context extraction orpattern analysis are included in the contextual information portfolio(e.g., user profile of George) (e.g., operation 225). The service thendetermines whether there are more frames in the video to process (e.g.,operation 230).

Once the video is processed, e.g., with respect to George, the serviceperforms a search to locate items, such as the sports jacket. The searchresults are weighted by the user profile. A subset of the search resultsare selected and communicated to George, for example, in a deliverymessage (e.g., operation 235). For example, five possible targetedresults for George are found at two different retailers. The links tothese items are texted to George.

Once the delivery message is received by the user, the user may be givenan opportunity to indicate a desire for one or more of the itemsspecified in the delivery message (e.g., operation 240). In this usecase, George selects the Olive sports jacket and matching dark brownleather Oxford shoes from the first retailer. George also, in hisresponse, requests same day delivery. In response to George's selection,the order is fulfilled and delivered to George's office (e.g., operation245). In an example, Jill may be acknowledged (e.g., compensated) forproviding the video in the first place in connection with George'sacceptance of the delivery message. The method 200 may repeat theprocess for other users in the video (e.g., operation 210).

FIG. 3 illustrates an example of a method 300 for computer visionassisted item search, according to an embodiment. The operations of themethod 300 are performed on computing hardware, such as that describedabove with respect to FIG. 1 or below with respect to FIG. 4 (e.g.,circuit sets).

At operation 305, a composite image is obtained from a sensor. Theobtained composite image includes both visual data and depth data.

At operation 310, the composite image is filtered to isolate a clothingarticle represented in the composite image. In an example, the compositeimage is filtered at a computing system remote from the sensor.

At operation 315, a classifier is applied to the depth data to produce aset of clothing attributes that correspond to the clothing article. Inan example, the classifier is applied at a computing system remote fromthe sensor.

In an example, the set of clothing attributes include at least one of asize, a shape, or a texture. In an example, the set of clothingattributes includes a fabric, and wherein the classifier correlates atexture to the fabric using the depth data. In an example, the set ofclothing attributes includes a clothing type provided by the filter.That is, the filter of operation 315 may identify the type of clothingduring its isolation of the clothing from the composite image and passthe identification to the classifier. In an example, the classifierscales a representation of the clothing article in the composite imageusing the depth data to produce an estimate of absolute measurements ofthe clothing article. In an example, the classifier converts theestimate of absolute measurements to a clothing article size. In anexample, classifier directly converts the depth data into a clothingarticle size (e.g., without first determining an absolute size of theclothing).

At operation 320, the clothing attributes may be provided to a remotedevice. In an example, the method 300 may extended to update a userprofile with the clothing attributes, the clothing attributescorresponding to a clothing item object in a data structure of the userprofile. In an example, these clothing attributes include at least oneof a color or a pattern. In an example, the user profile includes acontext data structure. This context data structure may include a set offields that may correspond to one or more of geographic location,landmark, event, or companion.

In an example, the method 300 may be extended to search for additionalclothing items using the clothing attributes and the user profile. In anexample, the search results may be provided to a third party (such as aretailer). In an example, searching for additional clothing itemsincludes applying a restriction that the additional clothing itemsdiffer from the clothing article in at least one of color, pattern,style, or type. In an example, where additional clothing items differ intype, the additional clothing items are filtered to combine with theclothing article, originally classified from the media, to add to aclass of outfits (e.g., a tie that goes with an observed blouse, pursewith dress, belt with shoes, etc.).

FIG. 4 illustrates a block diagram of an example machine 400 upon whichany one or more of the techniques (e.g., methodologies) discussed hereinmay perform. In alternative embodiments, the machine 400 may operate asa standalone device or may be connected (e.g., networked) to othermachines. In a networked deployment, the machine 400 may operate in thecapacity of a server machine, a client machine, or both in server-clientnetwork environments. In an example, the machine 400 may act as a peermachine in peer-to-peer (P2P) (or other distributed) networkenvironment. The machine 400 may be a personal computer (PC), a tabletPC, a set-top box (STB), a personal digital assistant (PDA), a mobiletelephone, a web appliance, a network router, switch or bridge, or anymachine capable of executing instructions (sequential or otherwise) thatspecify actions to be taken by that machine. Further, while only asingle machine is illustrated, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions to perform any one or more ofthe methodologies discussed herein, such as cloud computing, software asa service (SaaS), other computer cluster configurations.

Examples, as described herein, may include, or may operate by, logic ora number of components, or mechanisms. Circuit sets are a collection ofcircuits implemented in tangible entities that include hardware (e.g.,simple circuits, gates, logic, etc.). Circuit set membership may beflexible over time and underlying hardware variability. Circuit setsinclude members that may, alone or in combination, perform specifiedoperations when operating. In an example, hardware of the circuit setmay be immutably designed to carry out a specific operation (e.g.,hardwired). In an example, the hardware of the circuit set may includevariably connected physical components (e.g., execution units,transistors, simple circuits, etc.) including a computer readable mediumphysically modified (e.g., magnetically, electrically, moveableplacement of invariant massed particles, etc.) to encode instructions ofthe specific operation. In connecting the physical components, theunderlying electrical properties of a hardware constituent are changed,for example, from an insulator to a conductor or vice versa. Theinstructions enable embedded hardware (e.g., the execution units or aloading mechanism) to create members of the circuit set in hardware viathe variable connections to carry out portions of the specific operationwhen in operation. Accordingly, the computer readable medium iscommunicatively coupled to the other components of the circuit setmember when the device is operating. In an example, any of the physicalcomponents may be used in more than one member of more than one circuitset. For example, under operation, execution units may be used in afirst circuit of a first circuit set at one point in time and reused bya second circuit in the first circuit set, or by a third circuit in asecond circuit set at a different time.

Machine (e.g., computer system) 400 may include a hardware processor 402(e.g., a central processing unit (CPU), a graphics processing unit(GPU), a hardware processor core, or any combination thereof), a mainmemory 404 and a static memory 406, some or all of which may communicatewith each other via an interlink (e.g., bus) 408. The machine 400 mayfurther include a display unit 410, an alphanumeric input device 412(e.g., a keyboard), and a user interface (UI) navigation device 414(e.g., a mouse). In an example, the display unit 410, input device 412and UI navigation device 414 may be a touch screen display. The machine400 may additionally include a storage device (e.g., drive unit) 416, asignal generation device 418 (e.g., a speaker), a network interfacedevice 420, and one or more sensors 421, such as a global positioningsystem (GPS) sensor, compass, accelerometer, or other sensor. Themachine 400 may include an output controller 428, such as a serial(e.g., universal serial bus (USB), parallel, or other wired or wireless(e.g., infrared (IR), near field communication (NFC), etc.) connectionto communicate or control one or more peripheral devices (e.g., aprinter, card reader, etc.).

The storage device 416 may include a machine readable medium 422 onwhich is stored one or more sets of data structures or instructions 424(e.g., software) embodying or utilized by any one or more of thetechniques or functions described herein. The instructions 424 may alsoreside, completely or at least partially, within the main memory 404,within static memory 406, or within the hardware processor 402 duringexecution thereof by the machine 400. In an example, one or anycombination of the hardware processor 402, the main memory 404, thestatic memory 406, or the storage device 416 may constitute machinereadable media.

While the machine readable medium 422 is illustrated as a single medium,the term “machine readable medium” may include a single medium ormultiple media (e.g., a centralized or distributed database, and/orassociated caches and servers) configured to store the one or moreinstructions 424.

The term “machine readable medium” may include any medium that iscapable of storing, encoding, or carrying instructions for execution bythe machine 400 and that cause the machine 400 to perform any one ormore of the techniques of the present disclosure, or that is capable ofstoring, encoding or carrying data structures used by or associated withsuch instructions. Non-limiting machine readable medium examples mayinclude solid-state memories, and optical and magnetic media. In anexample, a massed machine readable medium comprises a machine readablemedium with a plurality of particles having invariant (e.g., rest) mass.Accordingly, massed machine-readable media are not transitorypropagating signals. Specific examples of massed machine readable mediamay include: non-volatile memory, such as semiconductor memory devices(e.g., Electrically Programmable Read-Only Memory (EPROM), ElectricallyErasable Programmable Read-Only Memory (EEPROM)) and flash memorydevices; magnetic disks, such as internal hard disks and removabledisks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

The instructions 424 may further be transmitted or received over acommunications network 426 using a transmission medium via the networkinterface device 420 utilizing any one of a number of transfer protocols(e.g., frame relay, internet protocol (IP), transmission controlprotocol (TCP), user datagram protocol (UDP), hypertext transferprotocol (HTTP), etc.). Example communication networks may include alocal area network (LAN), a wide area network (WAN), a packet datanetwork (e.g., the Internet), mobile telephone networks (e.g., cellularnetworks), Plain Old Telephone (POTS) networks, and wireless datanetworks (e.g., Institute of Electrical and Electronics Engineers (IEEE)802.11 family of standards known as Wi-Fi®, IEEE 802.16 family ofstandards known as WiMax®), IEEE 802.15.4 family of standards,peer-to-peer (P2P) networks, among others. In an example, the networkinterface device 420 may include one or more physical jacks (e.g.,Ethernet, coaxial, or phone jacks) or one or more antennas to connect tothe communications network 426. In an example, the network interfacedevice 420 may include a plurality of antennas to wirelessly communicateusing at least one of single-input multiple-output (SIMO),multiple-input multiple-output (MIMO), or multiple-input single-output(MISO) techniques. The term “transmission medium” shall be taken toinclude any intangible medium that is capable of storing, encoding orcarrying instructions for execution by the machine 400, and includesdigital or analog communications signals or other intangible medium tofacilitate communication of such software.

ADDITIONAL NOTES & EXAMPLES

Example 1 is a system for computer vision assisted item search, thesystem comprising: an interface to obtain a composite image from asensor, the composite image including visual data and depth data; afilter to isolate a clothing article represented in the composite image;a classifier to: accept the depth data; and produce a set of clothingattributes that correspond to the clothing article; and a transceiver toprovide the clothing attributes to a remote device.

In Example 2, the subject matter of Example 1 optionally includes,wherein at least one of the filter or the classifier are at a computingsystem remote from the sensor array.

In Example 3, the subject matter of any one or more of Examples 1-2optionally include, wherein the set of clothing attributes include atleast one of a size, a shape, or a texture.

In Example 4, the subject matter of Example 3 optionally includes,wherein the set of clothing attributes include a fabric, and wherein theclassifier is to correlate a texture to the fabric using the depth data.

In Example 5, the subject matter of any one or more of Examples 3-4optionally include, wherein the set of clothing attributes include aclothing type provided by the filter, and wherein the classifier is to:scale a representation of the clothing article in the composite imageusing the depth data to produce an estimate of absolute measurements ofthe clothing article; and convert the estimate of absolute measurementsto a clothing article size.

In Example 6, the subject matter of any one or more of Examples 3-5optionally include, wherein the set of clothing attributes include aclothing type provided by the filter, and wherein the classifier is todirectly convert the depth data into a clothing article size.

In Example 7, the subject matter of any one or more of Examples 1-6optionally include the remote device, the remote device to update a userprofile with the clothing attributes, the clothing attributescorresponding to a clothing item object in a data structure of the userprofile.

In Example 8, the subject matter of Example 7 optionally includes,wherein the clothing attributes include at least one of a color or apattern.

In Example 9, the subject matter of any one or more of Examples 7-8optionally include, wherein the user profile includes a context datastructure, the context data structure including a set of fields, the setof fields corresponding to at least one of geographic location,landmark, event, or companion.

In Example 10, the subject matter of any one or more of Examples 7-9optionally include a search engine, the search engine to search foradditional clothing items using the clothing attributes and the userprofile.

In Example 11, the subject matter of Example 10 optionally includes,wherein to search for additional clothing items includes the searchengine to apply a restriction that the additional clothing items differfrom the clothing article in at least one of color, pattern, style, ortype.

In Example 12, the subject matter of Example 11 optionally includes,wherein the additional clothing items differ in type, and wherein theadditional clothing items are filtered to combine with the clothingarticle to add to a class of outfits.

In Example 13, the subject matter of any one or more of Examples 10-12optionally include, wherein the search engine is to provide the searchresults to a third party.

Example 14 is a method for computer vision assisted item search, themethod comprising: obtaining a composite image from a sensor array, thecomposite image including visual data and depth data; filtering thecomposite image to isolate a clothing article represented in thecomposite image; applying a classifier to the depth data to produce aset of clothing attributes that correspond to the clothing article; andproviding the clothing attributes to a remote device.

In Example 15, the subject matter of Example 14 optionally includes,wherein at least one of filtering the composite image or applying theclassifier occur at a computing system remote from the sensor.

In Example 16, the subject matter of any one or more of Examples 14-15optionally include, wherein the set of clothing attributes include atleast one of a size, a shape, or a texture.

In Example 17, the subject matter of Example 16 optionally includes,wherein the set of clothing attributes include a fabric, and wherein theclassifier correlates a texture to the fabric using the depth data.

In Example 18, the subject matter of any one or more of Examples 16-17optionally include, wherein the set of clothing attributes include aclothing type provided by the filter, and wherein the classifier scalesa representation of the clothing article in the composite image usingthe depth data to produce an estimate of absolute measurements of theclothing article, and wherein the classifier converts the estimate ofabsolute measurements to a clothing article size.

In Example 19, the subject matter of any one or more of Examples 16-18optionally include, wherein the set of clothing attributes include aclothing type provided by the filter, and wherein the classifierdirectly converts the depth data into a clothing article size.

In Example 20, the subject matter of any one or more of Examples 14-19optionally include updating a user profile with the clothing attributes,the clothing attributes corresponding to a clothing item object in adata structure of the user profile.

In Example 21, the subject matter of Example 20 optionally includes,wherein the clothing attributes include at least one of a color or apattern.

In Example 22, the subject matter of any one or more of Examples 20-21optionally include, wherein the user profile includes a context datastructure, the context data structure including a set of fields, the setof fields corresponding to at least one of geographic location,landmark, event, or companion.

In Example 23, the subject matter of any one or more of Examples 20-22optionally include searching for additional clothing items using theclothing attributes and the user profile.

In Example 24, the subject matter of Example 23 optionally includes,wherein searching for additional clothing items includes applying arestriction that the additional clothing items differ from the clothingarticle in at least one of color, pattern, style, or type.

In Example 25, the subject matter of Example 24 optionally includes,wherein the additional clothing items differ in type, and wherein theadditional clothing items are filtered to combine with the clothingarticle to add to a class of outfits.

In Example 26, the subject matter of any one or more of Examples 23-25optionally include providing the search results to a third party.

Example 27 is a system comprising means to perform any method ofExamples 14-26.

Example 28 is a machine readable medium including instructions that,when executed by a machine, cause the machine to perform any method ofExamples 14-26.

Example 29 is a machine readable medium including instructions forcomputer vision assisted item search, the instructions, when executed bya machine, cause the machine to perform operations comprising: obtaininga composite image from a sensor array, the composite image includingvisual data and depth data; filtering the composite image to isolate aclothing article represented in the composite image; applying aclassifier to the depth data to produce a set of clothing attributesthat correspond to the clothing article; and providing the clothingattributes to a remote device.

In Example 30, the subject matter of Example 29 optionally includes,wherein at least one of filtering the composite image or applying theclassifier occur at a computing system remote from the sensor.

In Example 31, the subject matter of any one or more of Examples 29-30optionally include, wherein the set of clothing attributes include atleast one of a size, a shape, or a texture.

In Example 32, the subject matter of Example 31 optionally includes,wherein the set of clothing attributes include a fabric, and wherein theclassifier correlates a texture to the fabric using the depth data.

In Example 33, the subject matter of any one or more of Examples 31-32optionally include, wherein the set of clothing attributes include aclothing type provided by the filter, and wherein the classifier scalesa representation of the clothing article in the composite image usingthe depth data to produce an estimate of absolute measurements of theclothing article, and wherein the classifier converts the estimate ofabsolute measurements to a clothing article size.

In Example 34, the subject matter of any one or more of Examples 31-33optionally include, wherein the set of clothing attributes include aclothing type provided by the filter, and wherein the classifierdirectly converts the depth data into a clothing article size.

In Example 35, the subject matter of any one or more of Examples 29-34optionally include, wherein the operations comprise updating a userprofile with the clothing attributes, the clothing attributescorresponding to a clothing item object in a data structure of the userprofile.

In Example 36, the subject matter of Example 35 optionally includes,wherein the clothing attributes include at least one of a color or apattern.

In Example 37, the subject matter of any one or more of Examples 35-36optionally include, wherein the user profile includes a context datastructure, the context data structure including a set of fields, the setof fields corresponding to at least one of geographic location,landmark, event, or companion.

In Example 38, the subject matter of any one or more of Examples 35-37optionally include, wherein the operations comprise comprising searchingfor additional clothing items using the clothing attributes and the userprofile.

In Example 39, the subject matter of Example 38 optionally includes,wherein searching for additional clothing items includes applying arestriction that the additional clothing items differ from the clothingarticle in at least one of color, pattern, style, or type.

In Example 40, the subject matter of Example 39 optionally includes,wherein the additional clothing items differ in type, and wherein theadditional clothing items are filtered to combine with the clothingarticle to add to a class of outfits.

In Example 41, the subject matter of any one or more of Examples 38-40optionally include, wherein the operations comprise comprising providingthe search results to a third party.

Example 42 is a system for computer vision assisted item search, thesystem comprising: means for obtaining a composite image from a sensorarray, the composite image including visual data and depth data; meansfor filtering the composite image to isolate a clothing articlerepresented in the composite image; means for applying a classifier tothe depth data to produce a set of clothing attributes that correspondto the clothing article; and means for providing the clothing attributesto a remote device.

In Example 43, the subject matter of Example 42 optionally includes,wherein at least one of filtering the composite image or applying theclassifier occur at a computing system remote from the sensor.

In Example 44, the subject matter of any one or more of Examples 42-43optionally include, wherein the set of clothing attributes include atleast one of a size, a shape, or a texture.

In Example 45, the subject matter of Example 44 optionally includes,wherein the set of clothing attributes include a fabric, and wherein theclassifier correlates a texture to the fabric using the depth data.

In Example 46, the subject matter of any one or more of Examples 44-45optionally include, wherein the set of clothing attributes include aclothing type provided by the filter, and wherein the classifier scalesa representation of the clothing article in the composite image usingthe depth data to produce an estimate of absolute measurements of theclothing article, and wherein the classifier converts the estimate ofabsolute measurements to a clothing article size.

In Example 47, the subject matter of any one or more of Examples 44-46optionally include, wherein the set of clothing attributes include aclothing type provided by the filter, and wherein the classifierdirectly converts the depth data into a clothing article size.

In Example 48, the subject matter of any one or more of Examples 42-47optionally include means for updating a user profile with the clothingattributes, the clothing attributes corresponding to a clothing itemobject in a data structure of the user profile.

In Example 49, the subject matter of Example 48 optionally includes,wherein the clothing attributes include at least one of a color or apattern.

In Example 50, the subject matter of any one or more of Examples 48-49optionally include, wherein the user profile includes a context datastructure, the context data structure including a set of fields, the setof fields corresponding to at least one of geographic location,landmark, event, or companion.

In Example 51, the subject matter of any one or more of Examples 48-50optionally include means for searching for additional clothing itemsusing the clothing attributes and the user profile.

In Example 52, the subject matter of Example 51 optionally includes,wherein searching for additional clothing items includes means forapplying a restriction that the additional clothing items differ fromthe clothing article in at least one of color, pattern, style, or type.

In Example 53, the subject matter of Example 52 optionally includes,wherein the additional clothing items differ in type, and wherein theadditional clothing items are filtered to combine with the clothingarticle to add to a class of outfits.

In Example 54, the subject matter of any one or more of Examples 51-53optionally include means for providing the search results to a thirdparty.

The above detailed description includes references to the accompanyingdrawings, which form a part of the detailed description. The drawingsshow, by way of illustration, specific embodiments that may bepracticed. These embodiments are also referred to herein as “examples.”Such examples may include elements in addition to those shown ordescribed. However, the present inventors also contemplate examples inwhich only those elements shown or described are provided. Moreover, thepresent inventors also contemplate examples using any combination orpermutation of those elements shown or described (or one or more aspectsthereof), either with respect to a particular example (or one or moreaspects thereof), or with respect to other examples (or one or moreaspects thereof) shown or described herein.

All publications, patents, and patent documents referred to in thisdocument are incorporated by reference herein in their entirety, asthough individually incorporated by reference. In the event ofinconsistent usages between this document and those documents soincorporated by reference, the usage in the incorporated reference(s)should be considered supplementary to that of this document; forirreconcilable inconsistencies, the usage in this document controls.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In the appended claims, the terms “including” and“in which” are used as the plain-English equivalents of the respectiveterms “comprising” and “wherein.” Also, in the following claims, theterms “including” and “comprising” are open-ended, that is, a system,device, article, or process that includes elements in addition to thoselisted after such a term in a claim are still deemed to fall within thescope of that claim. Moreover, in the following claims, the terms“first,” “second,” and “third,” etc. are used merely as labels, and arenot intended to impose numerical requirements on their objects.

The above description is intended to be illustrative, and notrestrictive. For example, the above-described examples (or one or moreaspects thereof) may be used in combination with each other. Otherembodiments may be used, such as by one of ordinary skill in the artupon reviewing the above description. The Abstract is to allow thereader to quickly ascertain the nature of the technical disclosure andis submitted with the understanding that it will not be used tointerpret or limit the scope or meaning of the claims. Also, in theabove Detailed Description, various features may be grouped together tostreamline the disclosure. This should not be interpreted as intendingthat an unclaimed disclosed feature is essential to any claim. Rather,inventive subject matter may lie in less than all features of aparticular disclosed embodiment. Thus, the following claims are herebyincorporated into the Detailed Description, with each claim standing onits own as a separate embodiment. The scope of the embodiments should bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

What is claimed is:
 1. An apparatus comprising: memory; machine readableinstructions; and processor circuitry to execute the instructions to:identify a first article of clothing in an image, the image includingvisual data and depth data; produce a set of attributes corresponding tothe first article of clothing based on at least one of the visual dataor the depth data; select a second article of clothing from a databasebased on the set of attributes, the second article of clothing to bedifferent than the first article of clothing; and provide a message to auser that identifies the second article of clothing.
 2. The apparatus ofclaim 1, wherein the processor circuitry is to identify the user in theimage based on an analysis of the image.
 3. The apparatus of claim 1,wherein the processor circuitry is to select the second article ofclothing using artificial intelligence.
 4. The apparatus of claim 1,wherein the processor circuitry is to select the second article ofclothing based on preferences in a profile associated with the user. 5.The apparatus of claim 4, wherein the processor circuitry is to at leastone of generate or update the preferences based on an analysis ofadditional images of the user.
 6. The apparatus of claim 1, wherein theprocessor circuitry is to select the second article of clothing suchthat the first and second articles of clothing can be combined to createan outfit.
 7. The apparatus of claim 1, wherein the processor circuitryis to select the second article of clothing based on a fashion trend. 8.The apparatus of claim 1, wherein the set of attributes includes one ormore of a color, a pattern, a shape, a size, a texture, a fabric, astyle, or a type of the first article of clothing.
 9. At least onemachine readable storage device comprising instructions that, whenexecuted, cause processor circuitry to at least: identify a firstarticle of clothing in an image, the image including visual data anddepth data; produce a set of attributes corresponding to the firstarticle of clothing based on at least one of the visual data or thedepth data; select a second article of clothing from a database based onthe set of attributes, the second article of clothing to be differentthan the first article of clothing; and provide a message to a user thatidentifies of the second article of clothing.
 10. The at least onemachine readable storage device of claim 9, wherein the instructionscause the processor circuitry to identify the user in the image based onan analysis of the image.
 11. The at least one machine readable storagedevice of claim 9, wherein the instructions cause the processorcircuitry to select the second article of clothing using artificialintelligence.
 12. The at least one machine readable storage device ofclaim 9, wherein the instructions cause the processor circuitry toselect the second article of clothing based on preferences in a profileassociated with the user.
 13. The at least one machine readable storagedevice of claim 12, wherein the instructions cause the processorcircuitry to at least one of generate or update the preferences based onan analysis of additional images of the user.
 14. The at least onemachine readable storage device of claim 9, wherein the instructionscause the processor circuitry to select the second article of clothingsuch that the first and second articles of clothing can be combined tocreate an outfit.
 15. The at least one machine readable storage deviceof claim 9, wherein the instructions cause the processor circuitry toselect the second article of clothing based on a fashion trend.
 16. Amethod comprising: identifying a first article of clothing in an image,the image including visual data and depth data; producing a set ofattributes corresponding to the first article of clothing based on atleast one of the visual data or the depth data; selecting, by executingan instruction with processor circuitry, a second article of clothingfrom a database based on the set of attributes, the second article ofclothing to be different than the first article of clothing; and providea message to a user that includes an identification of the secondarticle of clothing.
 17. The method of claim 16, further includingidentifying the user in the image based on an analysis of the image. 18.The method of claim 16, wherein the selecting of the second article ofclothing is based on preferences in a profile associated with the user.19. The method of claim 18, further including at least one of generatingor updating the preferences based on an analysis of additional images ofthe user.
 20. The method of claim 16, wherein the selecting of thesecond article of clothing is such that the first and second articles ofclothing can be combined to create an outfit.
 21. The method of claim16, wherein the selecting of the second article of clothing is based ona fashion trend.